Stock index forecasting based on a hybrid model
Stock index forecasting based on a hybrid model
复制标题
基于混合模型的股指预测
DOI：
10.1016/j.omega.2011.07.008
复制
发表时间：
2012-12-01
影响因子：
6.9
通讯作者：
Guo, Shu-Po
中科院分区：
文献类型：
--
作者：
Wang, Ju-Jie;Wang, Jian-Zhou;Guo, Shu-Po
预测股市价格指数是一项具有挑战性的任务。指数平滑模型（ESM）、自回归积分移动平均模型（ARIMA）和反向传播神经网络（BPNN）可用于基于时间序列进行预测。在本文中，提出了一种结合 ESM、ARIMA 和 BPNN 的混合方法，它是所有三种模型中最有利的。所提出的混合模型（PHM）的权重由遗传算法（GA）确定。以深圳综合指数（SZII）收盘和道琼斯工业平均指数（DJIAI）开盘为例来评估 PHM 的表现。数值结果表明，所提出的模型优于所有传统模型，包括ESM、ARIMA、BPNN、等权混合模型（EWH）和随机游走模型（RWM）。 (C) 2011 Elsevier Ltd. 保留所有权利。
Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network (BPNN) can be used to make forecasts based on time series. In this paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the proposed hybrid model (PHM) is determined by genetic algorithm (GA). The closing of the Shenzhen Integrated Index (SZII) and opening of the Dow Jones Industrial Average Index (DJIAI) are used as illustrative examples to evaluate the performances of the PHM. Numerical results show that the proposed model outperforms all traditional models, including ESM, ARIMA, BPNN, the equal weight hybrid model (EWH), and the random walk model (RWM). (C) 2011 Elsevier Ltd. All rights reserved.